Separate model connections from data and user experience to evaluate local LLMs, private endpoints or commercial APIs by policy and tests. Confirm each model before production use.
Putting it into practice
Compare models on the same questions for language, quality, cost and latency. Record versions and routing rules. Do not silently fall back to an external API when local service fails unless policy permits.
Before you begin
- Shared test set
- Model versions
- Fallback rules
Capabilities and deployment require project-level confirmation. No unsupported certification, ROI or customer claims are made.
Frequently asked questions
Does data have to leave the network?
It depends on deployment and connected services. Map model, OCR, embedding, backup and log traffic before confirming the boundary.
Can an AI answer be trusted immediately?
Check the original, completeness and version, especially for legal, accounting and consequential decisions. Citations support review but do not guarantee accuracy.
How should a project begin?
Choose one defined use case, approved documents, owners and acceptance criteria. Test answer quality, permissions and cost with a small group before scaling.
References
Public sources explain principles; they do not certify or endorse RAGBOX.
- NIST AI Risk Management Framework
- Microsoft: Retrieval-augmented generation
- OWASP: RAG Security Cheat Sheet
- OWASP: Prompt Injection Prevention
Expert review is required before operational use. This is not case-specific legal or tax advice.